Today we are officially launching SuperDuperDB, an open-source framework for integrating AI directly with major databases, including streaming inference, scalable model training, and vector search, with release of v0.1. on GitHub and on ProductHunt.

SuperDuperDB is not a database. It transforms your favorite database into an AI development and deployment environment (𝘮𝘢𝘬𝘪𝘯𝘨 𝘪𝘵 𝘴𝘶𝘱𝘦𝘳-𝘥𝘶𝘱𝘦𝘳).

SuperDuperDB eliminates complex MLOps pipelines, specialized vector databases - and the need to migrate and duplicate data by integrating AI at the data's source, directly on top of your existing data infrastructure. This massively simplifies building and managing AI applications.

SuperDuperDB provides a simple Python interface, but allows experts to drill down to any level of implementation detail such as models weights or training details.

Today’s release comes with the full integration of major SQL databases as well as further MongoDB support: PostgreSQL, MySQL, SQLite, DuckDB, Snowflake, BigQuery, ClickHouse, DataFusion, Druid, Impala, MSSQL, Oracle, pandas, Polars, PySpark, and Trino.

Currently Supported AI: Any model from PyTorch, Sklearn, HuggingFace as well as AI APIs such as OpenAI, Anthrophic, Cohere.

A few useful links: - Our website: https://superduperdb.com - Getting started docs: https://docs.superduperdb.com/docs/category/get-started/ - Our repo on Github: https://github.com/SuperDuperDB/superduperdb

Check the uses-cases that we have already implemented here https://docs.superduperdb.com/docs/category/use-cases as well as apps built by the community here https://github.com/SuperDuperDB/superduper-community-apps and try all of them with Jupyter your browser https://demo.superduperdb.com/

For more information about SuperDuperDB and why we believe it is much needed, read the blog post https://docs.superduperdb.com/blog/superduperdb-the-open-sou...

We are keen to hear your feedback!

All the best, Timo